Henry A.

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Top Subject Matter

Financial Analysis

Energy

Data Analysis

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Document

Text

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Freelancer Overview

I have hands-on experience contributing to AI training data through two structured annotation projects on Lunor AI. In the first, I labelled Airbnb listing data by classifying each property against a traveller profile taxonomy (Solo, Couple, Family, Group), applying consistent judgement across varied listing descriptions and amenity sets to produce clean, structured labels for downstream model training. In the second, I worked with educational reading comprehension datasets, answering multiple-choice questions grounded in short English passages and evaluating text difficulty, question difficulty, and overall content quality, directly supporting the development of adaptive learning systems. What sets my contributions apart is the analytical rigour I bring from my background in Mathematics and Statistics, combined with active work in data analytics. I am comfortable assessing ambiguity, applying consistent classification logic, and providing calibrated quality evaluations, skills that translate directly into reliable, high-signal training data. My familiarity with structured datasets and attention to labelling consistency means I can contribute meaningfully to projects requiring both speed and accuracy.

Labeling Experience

TripFit Tags: Label Airbnb Listings by Trip Type

TextQuestion Answering

I labelled Airbnb listing data by classifying each property against a traveller profile taxonomy (Solo, Couple, Family, Group), applying consistent judgement across varied listing descriptions and amenity sets to produce clean, structured labels for downstream model training. The objective of the work is to Create a clean, large-scale labeled dataset that teaches recommendation systems and travel agents which listings fit which traveler intent, enabling: - Better search/ranking filters (“best for remote work”, “best for families”, etc.) - Cold-start recommendations (when little user history exists) - Faster matching between trip context and listing suitability

2026 - 2026

LiteracyForge : Building Smarter AI Tutors

TextQuestion Answering

I worked with educational reading comprehension datasets, answering multiple-choice questions grounded in short English passages and evaluating text difficulty, question difficulty, and overall content quality, directly supporting the development of adaptive learning systems. The main objective is to gather high-quality human responses and evaluations of comprehension questions related to short English texts. These contributions will help improve AI-driven educational systems that adapt to learner needs, build literacy skills, and improve accessibility worldwide.

2025 - 2025

Education

Work History